Enhancement of Unmanned Aerial Vehicle Image with Shadow Removal Based on Optimized Retinex Algorithm

• Published in: Wireless communications and mobile computing 2022, Vol.2022, p.1-9
• Main Authors: Xi, Wenfei, Zuo, Xiaoqing, Sangaiah, Arun Kumar
• Format: Article
• Language: English
• Subjects: Algorithms; Brightness; Darkness; Equalization; Feature extraction; Fuzzy sets; Gamma function; Histograms; Image acquisition; Image enhancement; Methods; Object recognition; Pixels; Random variables; Remote sensing; Retinex (algorithm); Shadows; Unmanned aerial vehicles
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/3204407

Images taken by UAVs have shadows due to terrain factors. The image pixel brightness of the shadow areas is compressed, and the information is deficient, which impacts the recognition of image information and thus limits the subsequent image application. Therefore, the shadow removal of the image is crucial. Image enhancement algorithm is capable of improving the whole and partial contrasts of images, highlighting detail information, and removing shadows. Three classical optical image enhancement algorithms are analyzed. The analysis results show that image would be enhanced excessively after the histogram equalization algorithm to the shadow image enhancement. The pixel brightness are compressed by the Mask homogenization algorithm enhancement and uneven brightness in some areas after the enhancement of the traditional Retinex algorithm. Using the Retinex enhancement algorithm, this study proposes a combination algorithm to remove the shadow of the UAV remote sensing image. The proposed algorithm integrates the Retinex algorithm with the two-dimensional (2D) gamma function to remove the brightness colour of the UAV image, so it is capable of removing the shadow area of the UAV image and correcting the uneven darkness attributed to the image enhancement. The acquired UAV image is used to perform the experiment, and it is integrated with the LOG algorithm to extract the enhanced image features. As indicated by the experimental results, the integrated algorithm is proved with better performance to remove the UAV image shadow. The shadow areas of the features cannot be extracted in the original image, but after using the new algorithm to remove the shadow, the ground edge features can be clearly extracted.